Computes the gradients of 3-D convolution with respect to the input.
tf.raw_ops.Conv3DBackpropInputV2(
input_sizes,
filter,
out_backprop,
strides,
padding,
data_format='NDHWC',
dilations=[1, 1, 1, 1, 1],
name=None
)
Args |
input_sizes
|
A Tensor . Must be one of the following types: int32 , int64 .
An integer vector representing the tensor shape of input ,
where input is a 5-D
[batch, depth, rows, cols, in_channels] tensor.
|
filter
|
A Tensor . Must be one of the following types: half , bfloat16 , float32 , float64 .
Shape [depth, rows, cols, in_channels, out_channels] .
in_channels must match between input and filter .
|
out_backprop
|
A Tensor . Must have the same type as filter .
Backprop signal of shape [batch, out_depth, out_rows, out_cols,
out_channels] .
|
strides
|
A list of ints that has length >= 5 .
1-D tensor of length 5. The stride of the sliding window for each
dimension of input . Must have strides[0] = strides[4] = 1 .
|
padding
|
A string from: "SAME", "VALID" .
The type of padding algorithm to use.
|
data_format
|
An optional string from: "NDHWC", "NCDHW" . Defaults to "NDHWC" .
The data format of the input and output data. With the
default format "NDHWC", the data is stored in the order of:
[batch, in_depth, in_height, in_width, in_channels].
Alternatively, the format could be "NCDHW", the data storage order is:
[batch, in_channels, in_depth, in_height, in_width].
|
dilations
|
An optional list of ints . Defaults to [1, 1, 1, 1, 1] .
1-D tensor of length 5. The dilation factor for each dimension of
input . If set to k > 1, there will be k-1 skipped cells between each
filter element on that dimension. The dimension order is determined by the
value of data_format , see above for details. Dilations in the batch and
depth dimensions must be 1.
|
name
|
A name for the operation (optional).
|
Returns |
A Tensor . Has the same type as filter .
|